Home Work 5 - bakharia

Reading the dataset and rearranging the columns

ab_nyc <- read.csv("../../_data/AB_NYC_2019.csv") %>%
  relocate(c("latitude", "longitude", "id", "name", "host_id", "host_name", "neighbourhood_group", "neighbourhood", "room_type", "minimum_nights", "number_of_reviews", "last_review", "reviews_per_month", "calculated_host_listings_count", "availability_365"))

head(ab_nyc)

  latitude longitude   id
1 40.64749 -73.97237 2539
2 40.75362 -73.98377 2595
3 40.80902 -73.94190 3647
4 40.68514 -73.95976 3831
5 40.79851 -73.94399 5022
6 40.74767 -73.97500 5099
                                              name host_id
1               Clean & quiet apt home by the park    2787
2                            Skylit Midtown Castle    2845
3              THE VILLAGE OF HARLEM....NEW YORK !    4632
4                  Cozy Entire Floor of Brownstone    4869
5 Entire Apt: Spacious Studio/Loft by central park    7192
6        Large Cozy 1 BR Apartment In Midtown East    7322
    host_name neighbourhood_group neighbourhood       room_type
1        John            Brooklyn    Kensington    Private room
2    Jennifer           Manhattan       Midtown Entire home/apt
3   Elisabeth           Manhattan        Harlem    Private room
4 LisaRoxanne            Brooklyn  Clinton Hill Entire home/apt
5       Laura           Manhattan   East Harlem Entire home/apt
6       Chris           Manhattan   Murray Hill Entire home/apt
  minimum_nights number_of_reviews last_review reviews_per_month
1              1                 9  2018-10-19              0.21
2              1                45  2019-05-21              0.38
3              3                 0                            NA
4              1               270  2019-07-05              4.64
5             10                 9  2018-11-19              0.10
6              3                74  2019-06-22              0.59
  calculated_host_listings_count availability_365 price
1                              6              365   149
2                              2              355   225
3                              1              365   150
4                              1              194    89
5                              1                0    80
6                              1              129   200

Finding out the least expensive stay at a bnb by multiplying the cost/night with the min no days for staying

min_cost <- st_as_sf(as.data.frame(ab_nyc$minimum_nights * ab_nyc$price) 
                     %>% `colnames<-`(c("Minimum Cost of Stay")) 
                     %>% mutate(lat = ab_nyc$latitude, lon = ab_nyc$longitude) 
                     %>% select(lat, lon, `Minimum Cost of Stay`),
                    coords = c("lon", "lat"),
                     crs = "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"
                     )

Using summary to specify the affordability of each place (#1 <= 135, #2 <= 300, #3 <= 734, #4 > 734)

summary(min_cost)

 Minimum Cost of Stay          geometry    
 Min.   :      0.0    POINT        :48895  
 1st Qu.:    135.0    epsg:NA      :    0  
 Median :    300.0    +proj=long...:    0  
 Mean   :   1284.4                         
 3rd Qu.:    734.5                         
 Max.   :1170000.0

info <- summary(min_cost)
#as.numeric(str_extract(info[3], "[0-9]+"))
min_cost <- min_cost %>%
  mutate(Affordability = case_when(
    `Minimum Cost of Stay` <= as.numeric(str_extract(info[2], "[0-9]+")) ~ 1,
    `Minimum Cost of Stay` <= as.numeric(str_extract(info[3], "[0-9]+")) ~ 2,
    `Minimum Cost of Stay` <= as.numeric(str_extract(info[5], "[0-9]+")) ~ 3,
    `Minimum Cost of Stay` > as.numeric(str_extract(info[5], "[0-9]+")) ~ 4
  ))

Mapping all the places based on the affordability using the lat and long provided in the dataset using the mapview library

library(mapview)
min_cost %>%
  select(geometry, Affordability) %>%
  mapview(zcol = "Affordability")

Using groupby and summarise to find out the owners who own most no of bnbs in the city

                  #as.data.frame(table(ab_nyc$host_id)) %>% `colnames<-`(c("host_id","No. of Places"))
no_of_places <- as.data.frame(ab_nyc %>% group_by(host_id,host_name) %>% summarise(n = n()) %>% `colnames<-`(c("host_id", "host_name", "No. of Places")))
no_of_places %>%
  arrange(desc(`No. of Places` ))%>%
  slice(1:10)%>%
  ggplot(aes(host_name,`No. of Places`, fill=as.character(host_id))) +
  geom_bar(stat = "identity") + theme_minimal() + coord_flip() + 
  labs(x = "Host Name", fill = "Host ID")

Find the relation number of listings at place with the experience with the of owner(based on number of places owned). It is found that the avg_listings by the experienced owner(having multiple bnbs) is more. The graph from above remains the same

no_of_places %>%
  mutate(listings = sapply(host_id,function(x){return (sum(ab_nyc[ab_nyc$host_id == as.numeric(x), "calculated_host_listings_count"]))}))%>%
  mutate(avg_listings = listings/`No. of Places`) %>%
  arrange(desc(avg_listings)) %>%
  slice(1:10) %>%
  ggplot(aes(host_name,avg_listings, fill = as.character(host_id))) +
  geom_bar(stat = "identity") + theme_minimal() +
  labs(x = "Host Name", y = "Avg Listings", fill = "Host ID") + coord_flip()

On categorisingon the basis of privacy(shared room~ shared, private room~ Room, Entire Place ~ Entire), it is found that the most number of bnbs offer the entire place.

ab_nyc %>%
  mutate(Type = case_when(
    str_extract(room_type, "[^\\s]+") == "Shared" ~ "Shared",
    str_extract(room_type, "[^\\s]+") == "Private"~ "Room",
    str_extract(room_type, "[^\\s]+") == "Entire" ~ "House/Apt"
  )) %>%
  ggplot(aes(Type)) +
  geom_bar(aes(fill = Type)) + theme_minimal()

On categorising all the bnbs based on the neighbourhood that they are in, it is found that Manhattan has the most number of bnbs

ab_nyc %>%
  group_by(neighbourhood_group) %>%
  summarise(Count = n()) %>%
  arrange(desc(Count))%>%
  ggplot(aes(neighbourhood_group, Count, fill = neighbourhood_group)) +
  geom_bar(stat = "identity") + theme_minimal()